Overview

Dataset statistics

Number of variables38
Number of observations158429
Missing cells1802
Missing cells (%)< 0.1%
Duplicate rows5410
Duplicate rows (%)3.4%
Total size in memory45.9 MiB
Average record size in memory304.0 B

Variable types

Numeric6
Categorical32

Alerts

Dataset has 5410 (3.4%) duplicate rowsDuplicates
Age is highly overall correlated with EL_primary and 7 other fieldsHigh correlation
EL_higherprofessional_university is highly overall correlated with EL_secondary_higherHigh correlation
EL_primary is highly overall correlated with Age and 1 other fieldsHigh correlation
EL_secondary_higher is highly overall correlated with EL_higherprofessional_universityHigh correlation
Ethn_dutch is highly overall correlated with Ethn_nonwestern and 1 other fieldsHigh correlation
Ethn_nonwestern is highly overall correlated with Ethn_dutchHigh correlation
Ethn_western is highly overall correlated with Ethn_dutchHigh correlation
HHC_couple is highly overall correlated with Age and 1 other fieldsHigh correlation
HHC_couple_with_children is highly overall correlated with Age and 1 other fieldsHigh correlation
Main_moti_pickupdropoff_goods is highly overall correlated with Main_moti_sparetimeHigh correlation
Main_moti_sparetime is highly overall correlated with Main_moti_pickupdropoff_goodsHigh correlation
Moti_pickupdropoff_goods is highly overall correlated with Moti_sparetimeHigh correlation
Moti_sparetime is highly overall correlated with Moti_pickupdropoff_goodsHigh correlation
PW_no is highly overall correlated with Age and 3 other fieldsHigh correlation
PW_yesmorethan30h is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_benefits is highly overall correlated with Age and 2 other fieldsHigh correlation
UO_none is highly overall correlated with Age and 3 other fieldsHigh correlation
UO_student/scholar is highly overall correlated with Age and 1 other fieldsHigh correlation
PW_yeslessthan12h is highly imbalanced (76.0%)Imbalance
HHC_oneperson_with_children is highly imbalanced (65.3%)Imbalance
Ethn_western is highly imbalanced (52.8%)Imbalance
Moti_profession is highly imbalanced (88.8%)Imbalance
Moti_pickupdropoff_person is highly imbalanced (63.6%)Imbalance
Main_moti_profession is highly imbalanced (79.9%)Imbalance
Main_moti_pickupdropoff_person is highly imbalanced (68.0%)Imbalance
Starting_postalcode has 1802 (1.1%) missing valuesMissing
Number_of_cars_in_HH has 23917 (15.1%) zerosZeros

Reproduction

Analysis started2024-07-05 11:48:26.410092
Analysis finished2024-07-05 11:49:08.489854
Duration42.08 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

Starting_postalcode
Real number (ℝ)

MISSING 

Distinct3647
Distinct (%)2.3%
Missing1802
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean4332.982
Minimum1011
Maximum9998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:08.917303image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1011
5-th percentile1118
Q12551
median3645
Q36101
95-th percentile8911
Maximum9998
Range8987
Interquartile range (IQR)3550

Descriptive statistics

Standard deviation2364.595
Coefficient of variation (CV)0.54572002
Kurtosis-0.65324304
Mean4332.982
Median Absolute Deviation (MAD)1594
Skewness0.6030768
Sum6.7866197 × 108
Variance5591309.5
MonotonicityNot monotonic
2024-07-05T13:49:09.203018image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3011 683
 
0.4%
3511 550
 
0.3%
2492 444
 
0.3%
2611 363
 
0.2%
3584 359
 
0.2%
3991 355
 
0.2%
2496 326
 
0.2%
3512 325
 
0.2%
1181 325
 
0.2%
3012 321
 
0.2%
Other values (3637) 152576
96.3%
(Missing) 1802
 
1.1%
ValueCountFrequency (%)
1011 149
0.1%
1012 285
0.2%
1013 213
0.1%
1014 59
 
< 0.1%
1015 110
 
0.1%
1016 155
0.1%
1017 178
0.1%
1018 237
0.1%
1019 131
0.1%
1021 47
 
< 0.1%
ValueCountFrequency (%)
9998 5
 
< 0.1%
9997 2
 
< 0.1%
9995 6
 
< 0.1%
9993 1
 
< 0.1%
9991 15
< 0.1%
9989 9
 
< 0.1%
9988 2
 
< 0.1%
9984 1
 
< 0.1%
9983 14
< 0.1%
9982 26
< 0.1%

Gender_male
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
79545 
1
78884 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters158429
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Length

2024-07-05T13:49:09.403733image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:09.566623image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Most occurring characters

ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158429
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Most occurring scripts

ValueCountFrequency (%)
Common 158429
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 79545
50.2%
1 78884
49.8%

Age
Real number (ℝ)

HIGH CORRELATION 

Distinct93
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.680639
Minimum6
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:09.761573image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile12
Q127
median44
Q359
95-th percentile76
Maximum98
Range92
Interquartile range (IQR)32

Descriptive statistics

Standard deviation20.102409
Coefficient of variation (CV)0.46021324
Kurtosis-0.94141235
Mean43.680639
Median Absolute Deviation (MAD)16
Skewness0.069676456
Sum6920280
Variance404.10683
MonotonicityNot monotonic
2024-07-05T13:49:09.984857image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 3055
 
1.9%
50 2801
 
1.8%
52 2794
 
1.8%
31 2738
 
1.7%
54 2735
 
1.7%
53 2696
 
1.7%
43 2625
 
1.7%
44 2584
 
1.6%
30 2566
 
1.6%
42 2556
 
1.6%
Other values (83) 131279
82.9%
ValueCountFrequency (%)
6 1023
0.6%
7 1233
0.8%
8 1167
0.7%
9 1425
0.9%
10 1507
1.0%
11 1392
0.9%
12 1648
1.0%
13 1591
1.0%
14 1485
0.9%
15 1548
1.0%
ValueCountFrequency (%)
98 1
 
< 0.1%
97 5
 
< 0.1%
96 3
 
< 0.1%
95 5
 
< 0.1%
94 7
 
< 0.1%
93 27
 
< 0.1%
92 31
< 0.1%
91 40
< 0.1%
90 58
< 0.1%
89 71
< 0.1%

Number_of_cars_in_HH
Real number (ℝ)

ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3664102
Minimum0
Maximum10
Zeros23917
Zeros (%)15.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:10.175124image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile3
Maximum10
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.047915
Coefficient of variation (CV)0.76691101
Kurtosis16.665602
Mean1.3664102
Median Absolute Deviation (MAD)1
Skewness2.5744024
Sum216479
Variance1.0981259
MonotonicityNot monotonic
2024-07-05T13:49:10.344346image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 74795
47.2%
2 45949
29.0%
0 23917
 
15.1%
3 10061
 
6.4%
4 2242
 
1.4%
5 597
 
0.4%
10 536
 
0.3%
6 179
 
0.1%
7 68
 
< 0.1%
9 60
 
< 0.1%
ValueCountFrequency (%)
0 23917
 
15.1%
1 74795
47.2%
2 45949
29.0%
3 10061
 
6.4%
4 2242
 
1.4%
5 597
 
0.4%
6 179
 
0.1%
7 68
 
< 0.1%
8 25
 
< 0.1%
9 60
 
< 0.1%
ValueCountFrequency (%)
10 536
 
0.3%
9 60
 
< 0.1%
8 25
 
< 0.1%
7 68
 
< 0.1%
6 179
 
0.1%
5 597
 
0.4%
4 2242
 
1.4%
3 10061
 
6.4%
2 45949
29.0%
1 74795
47.2%

Ebike_in_HH
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0
106521 
1
51908 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters158429
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%

Length

2024-07-05T13:49:10.521707image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:10.677064image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%

Most occurring characters

ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158429
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%

Most occurring scripts

ValueCountFrequency (%)
Common 158429
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 106521
67.2%
1 51908
32.8%
Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9385782
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:10.827208image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median8
Q39
95-th percentile10
Maximum10
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6710352
Coefficient of variation (CV)0.38495425
Kurtosis-0.6163603
Mean6.9385782
Median Absolute Deviation (MAD)2
Skewness-0.66256318
Sum1099272
Variance7.1344289
MonotonicityNot monotonic
2024-07-05T13:49:10.993323image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 31956
20.2%
9 26811
16.9%
8 21394
13.5%
7 18105
11.4%
6 14959
9.4%
5 12702
 
8.0%
4 10684
 
6.7%
3 8533
 
5.4%
1 7643
 
4.8%
2 5642
 
3.6%
ValueCountFrequency (%)
1 7643
 
4.8%
2 5642
 
3.6%
3 8533
 
5.4%
4 10684
 
6.7%
5 12702
 
8.0%
6 14959
9.4%
7 18105
11.4%
8 21394
13.5%
9 26811
16.9%
10 31956
20.2%
ValueCountFrequency (%)
10 31956
20.2%
9 26811
16.9%
8 21394
13.5%
7 18105
11.4%
6 14959
9.4%
5 12702
 
8.0%
4 10684
 
6.7%
3 8533
 
5.4%
2 5642
 
3.6%
1 7643
 
4.8%

Trip_distance
Real number (ℝ)

Distinct1141
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.804487
Minimum0
Maximum3595
Zeros22
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:11.189822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q110
median30
Q372
95-th percentile380
Maximum3595
Range3595
Interquartile range (IQR)62

Descriptive statistics

Standard deviation183.9112
Coefficient of variation (CV)2.1186831
Kurtosis40.494092
Mean86.804487
Median Absolute Deviation (MAD)21
Skewness5.3684385
Sum13752348
Variance33823.329
MonotonicityNot monotonic
2024-07-05T13:49:11.421970image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 14878
 
9.4%
20 12040
 
7.6%
30 8988
 
5.7%
5 8192
 
5.2%
50 7598
 
4.8%
40 5733
 
3.6%
15 5249
 
3.3%
1 3865
 
2.4%
60 3527
 
2.2%
100 3407
 
2.2%
Other values (1131) 84952
53.6%
ValueCountFrequency (%)
0 22
 
< 0.1%
1 3865
2.4%
2 3371
2.1%
3 3378
2.1%
4 2524
 
1.6%
5 8192
5.2%
6 2123
 
1.3%
7 2104
 
1.3%
8 2700
 
1.7%
9 1377
 
0.9%
ValueCountFrequency (%)
3595 1
< 0.1%
3540 1
< 0.1%
2841 1
< 0.1%
2800 1
< 0.1%
2750 2
< 0.1%
2710 1
< 0.1%
2700 2
< 0.1%
2650 1
< 0.1%
2625 1
< 0.1%
2580 2
< 0.1%
Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
56484 
4.0
49082 
3.0
45583 
2.0
7280 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
1.0 56484
35.7%
4.0 49082
31.0%
3.0 45583
28.8%
2.0 7280
 
4.6%

Length

2024-07-05T13:49:11.638335image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:11.809027image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 56484
35.7%
4.0 49082
31.0%
3.0 45583
28.8%
2.0 7280
 
4.6%

Most occurring characters

ValueCountFrequency (%)
. 158429
33.3%
0 158429
33.3%
1 56484
 
11.9%
4 49082
 
10.3%
3 45583
 
9.6%
2 7280
 
1.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 158429
50.0%
1 56484
 
17.8%
4 49082
 
15.5%
3 45583
 
14.4%
2 7280
 
2.3%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 158429
33.3%
0 158429
33.3%
1 56484
 
11.9%
4 49082
 
10.3%
3 45583
 
9.6%
2 7280
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 158429
33.3%
0 158429
33.3%
1 56484
 
11.9%
4 49082
 
10.3%
3 45583
 
9.6%
2 7280
 
1.5%

Trip_starthour
Real number (ℝ)

Distinct28
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.496336
Minimum0
Maximum33
Zeros145
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.2 MiB
2024-07-05T13:49:11.981555image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile7
Q110
median14
Q317
95-th percentile20
Maximum33
Range33
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.1086525
Coefficient of variation (CV)0.30442726
Kurtosis-0.63186494
Mean13.496336
Median Absolute Deviation (MAD)3
Skewness0.046371033
Sum2138211
Variance16.881026
MonotonicityNot monotonic
2024-07-05T13:49:12.172431image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
14 13788
 
8.7%
17 13471
 
8.5%
16 13370
 
8.4%
8 13076
 
8.3%
15 12608
 
8.0%
13 11795
 
7.4%
12 11757
 
7.4%
11 11366
 
7.2%
10 11030
 
7.0%
9 9310
 
5.9%
Other values (18) 36858
23.3%
ValueCountFrequency (%)
0 145
 
0.1%
1 139
 
0.1%
2 120
 
0.1%
3 82
 
0.1%
4 139
 
0.1%
5 556
 
0.4%
6 2049
 
1.3%
7 6291
4.0%
8 13076
8.3%
9 9310
5.9%
ValueCountFrequency (%)
33 1
 
< 0.1%
26 2
 
< 0.1%
25 4
 
< 0.1%
24 56
 
< 0.1%
23 1574
 
1.0%
22 2293
 
1.4%
21 2817
 
1.8%
20 4812
3.0%
19 7119
4.5%
18 8659
5.5%

Part_of_sequence
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1
138504 
0
19925 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters158429
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

Length

2024-07-05T13:49:12.367380image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:12.530776image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

Most occurring characters

ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 158429
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

Most occurring scripts

ValueCountFrequency (%)
Common 158429
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 158429
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 138504
87.4%
0 19925
 
12.6%

PW_no
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
94114 
1.0
64315 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 94114
59.4%
1.0 64315
40.6%

Length

2024-07-05T13:49:12.687211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:12.837453image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 94114
59.4%
1.0 64315
40.6%

Most occurring characters

ValueCountFrequency (%)
0 252543
53.1%
. 158429
33.3%
1 64315
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252543
79.7%
1 64315
 
20.3%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252543
53.1%
. 158429
33.3%
1 64315
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252543
53.1%
. 158429
33.3%
1 64315
 
13.5%

PW_yeslessthan12h
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
152156 
1.0
 
6273

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 152156
96.0%
1.0 6273
 
4.0%

Length

2024-07-05T13:49:13.008869image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:13.171122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 152156
96.0%
1.0 6273
 
4.0%

Most occurring characters

ValueCountFrequency (%)
0 310585
65.3%
. 158429
33.3%
1 6273
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 310585
98.0%
1 6273
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 310585
65.3%
. 158429
33.3%
1 6273
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 310585
65.3%
. 158429
33.3%
1 6273
 
1.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
134738 
1.0
23691 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 134738
85.0%
1.0 23691
 
15.0%

Length

2024-07-05T13:49:13.321161image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:13.488069image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 134738
85.0%
1.0 23691
 
15.0%

Most occurring characters

ValueCountFrequency (%)
0 293167
61.7%
. 158429
33.3%
1 23691
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 293167
92.5%
1 23691
 
7.5%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 293167
61.7%
. 158429
33.3%
1 23691
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 293167
61.7%
. 158429
33.3%
1 23691
 
5.0%

PW_yesmorethan30h
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
94279 
1.0
64150 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 94279
59.5%
1.0 64150
40.5%

Length

2024-07-05T13:49:13.653559image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:13.804451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 94279
59.5%
1.0 64150
40.5%

Most occurring characters

ValueCountFrequency (%)
0 252708
53.2%
. 158429
33.3%
1 64150
 
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 252708
79.8%
1 64150
 
20.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 252708
53.2%
. 158429
33.3%
1 64150
 
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 252708
53.2%
. 158429
33.3%
1 64150
 
13.5%

UO_none
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
81881 
1.0
76548 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 81881
51.7%
1.0 76548
48.3%

Length

2024-07-05T13:49:13.979085image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:14.121071image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 81881
51.7%
1.0 76548
48.3%

Most occurring characters

ValueCountFrequency (%)
0 240310
50.6%
. 158429
33.3%
1 76548
 
16.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 240310
75.8%
1 76548
 
24.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 240310
50.6%
. 158429
33.3%
1 76548
 
16.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 240310
50.6%
. 158429
33.3%
1 76548
 
16.1%

UO_benefits
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
108798 
1.0
49631 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 108798
68.7%
1.0 49631
31.3%

Length

2024-07-05T13:49:14.287537image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:14.455293image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 108798
68.7%
1.0 49631
31.3%

Most occurring characters

ValueCountFrequency (%)
0 267227
56.2%
. 158429
33.3%
1 49631
 
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 267227
84.3%
1 49631
 
15.7%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 267227
56.2%
. 158429
33.3%
1 49631
 
10.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 267227
56.2%
. 158429
33.3%
1 49631
 
10.4%

UO_student/scholar
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
126179 
1.0
32250 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 126179
79.6%
1.0 32250
 
20.4%

Length

2024-07-05T13:49:14.621904image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:14.776373image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 126179
79.6%
1.0 32250
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 284608
59.9%
. 158429
33.3%
1 32250
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284608
89.8%
1 32250
 
10.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284608
59.9%
. 158429
33.3%
1 32250
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284608
59.9%
. 158429
33.3%
1 32250
 
6.8%

HHC_oneperson
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
129155 
1.0
29274 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 129155
81.5%
1.0 29274
 
18.5%

Length

2024-07-05T13:49:14.937327image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:15.087701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 129155
81.5%
1.0 29274
 
18.5%

Most occurring characters

ValueCountFrequency (%)
0 287584
60.5%
. 158429
33.3%
1 29274
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 287584
90.8%
1 29274
 
9.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 287584
60.5%
. 158429
33.3%
1 29274
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 287584
60.5%
. 158429
33.3%
1 29274
 
6.2%

HHC_couple
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
110321 
1.0
48108 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 110321
69.6%
1.0 48108
30.4%

Length

2024-07-05T13:49:15.253822image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:15.403739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110321
69.6%
1.0 48108
30.4%

Most occurring characters

ValueCountFrequency (%)
0 268750
56.5%
. 158429
33.3%
1 48108
 
10.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268750
84.8%
1 48108
 
15.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268750
56.5%
. 158429
33.3%
1 48108
 
10.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268750
56.5%
. 158429
33.3%
1 48108
 
10.1%

HHC_couple_with_children
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
87695 
1.0
70734 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 87695
55.4%
1.0 70734
44.6%

Length

2024-07-05T13:49:15.588329image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:15.737401image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 87695
55.4%
1.0 70734
44.6%

Most occurring characters

ValueCountFrequency (%)
0 246124
51.8%
. 158429
33.3%
1 70734
 
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 246124
77.7%
1 70734
 
22.3%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 246124
51.8%
. 158429
33.3%
1 70734
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 246124
51.8%
. 158429
33.3%
1 70734
 
14.9%

HHC_oneperson_with_children
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
148116 
1.0
 
10313

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 148116
93.5%
1.0 10313
 
6.5%

Length

2024-07-05T13:49:15.903742image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:16.053701image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 148116
93.5%
1.0 10313
 
6.5%

Most occurring characters

ValueCountFrequency (%)
0 306545
64.5%
. 158429
33.3%
1 10313
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 306545
96.7%
1 10313
 
3.3%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 306545
64.5%
. 158429
33.3%
1 10313
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 306545
64.5%
. 158429
33.3%
1 10313
 
2.2%

Ethn_dutch
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
1.0
123892 
0.0
34537 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 123892
78.2%
0.0 34537
 
21.8%

Length

2024-07-05T13:49:16.208947image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:16.370278image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 123892
78.2%
0.0 34537
 
21.8%

Most occurring characters

ValueCountFrequency (%)
0 192966
40.6%
. 158429
33.3%
1 123892
26.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 192966
60.9%
1 123892
39.1%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 192966
40.6%
. 158429
33.3%
1 123892
26.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 192966
40.6%
. 158429
33.3%
1 123892
26.1%

Ethn_western
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
142439 
1.0
15990 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 142439
89.9%
1.0 15990
 
10.1%

Length

2024-07-05T13:49:16.536935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:17.286755image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 142439
89.9%
1.0 15990
 
10.1%

Most occurring characters

ValueCountFrequency (%)
0 300868
63.3%
. 158429
33.3%
1 15990
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 300868
95.0%
1 15990
 
5.0%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 300868
63.3%
. 158429
33.3%
1 15990
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 300868
63.3%
. 158429
33.3%
1 15990
 
3.4%

Ethn_nonwestern
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
139882 
1.0
18547 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 139882
88.3%
1.0 18547
 
11.7%

Length

2024-07-05T13:49:17.471713image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:17.769079image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 139882
88.3%
1.0 18547
 
11.7%

Most occurring characters

ValueCountFrequency (%)
0 298311
62.8%
. 158429
33.3%
1 18547
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 298311
94.1%
1 18547
 
5.9%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 298311
62.8%
. 158429
33.3%
1 18547
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 298311
62.8%
. 158429
33.3%
1 18547
 
3.9%

EL_primary
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
136564 
1.0
21865 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 136564
86.2%
1.0 21865
 
13.8%

Length

2024-07-05T13:49:18.155066image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:18.352117image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 136564
86.2%
1.0 21865
 
13.8%

Most occurring characters

ValueCountFrequency (%)
0 294993
62.1%
. 158429
33.3%
1 21865
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 294993
93.1%
1 21865
 
6.9%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 294993
62.1%
. 158429
33.3%
1 21865
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 294993
62.1%
. 158429
33.3%
1 21865
 
4.6%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
138305 
1.0
20124 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 138305
87.3%
1.0 20124
 
12.7%

Length

2024-07-05T13:49:18.604832image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:18.856981image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 138305
87.3%
1.0 20124
 
12.7%

Most occurring characters

ValueCountFrequency (%)
0 296734
62.4%
. 158429
33.3%
1 20124
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 296734
93.6%
1 20124
 
6.4%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 296734
62.4%
. 158429
33.3%
1 20124
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 296734
62.4%
. 158429
33.3%
1 20124
 
4.2%

EL_secondary_higher
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
111685 
1.0
46744 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 111685
70.5%
1.0 46744
29.5%

Length

2024-07-05T13:49:19.036739image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:19.189456image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 111685
70.5%
1.0 46744
29.5%

Most occurring characters

ValueCountFrequency (%)
0 270114
56.8%
. 158429
33.3%
1 46744
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 270114
85.2%
1 46744
 
14.8%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 270114
56.8%
. 158429
33.3%
1 46744
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 270114
56.8%
. 158429
33.3%
1 46744
 
9.8%

EL_higherprofessional_university
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
88733 
1.0
69696 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 88733
56.0%
1.0 69696
44.0%

Length

2024-07-05T13:49:19.337499image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:19.488422image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 88733
56.0%
1.0 69696
44.0%

Most occurring characters

ValueCountFrequency (%)
0 247162
52.0%
. 158429
33.3%
1 69696
 
14.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 247162
78.0%
1 69696
 
22.0%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 247162
52.0%
. 158429
33.3%
1 69696
 
14.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 247162
52.0%
. 158429
33.3%
1 69696
 
14.7%

Moti_work
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
132655 
1.0
25774 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 132655
83.7%
1.0 25774
 
16.3%

Length

2024-07-05T13:49:19.655016image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:19.823451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 132655
83.7%
1.0 25774
 
16.3%

Most occurring characters

ValueCountFrequency (%)
0 291084
61.2%
. 158429
33.3%
1 25774
 
5.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 291084
91.9%
1 25774
 
8.1%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 291084
61.2%
. 158429
33.3%
1 25774
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 291084
61.2%
. 158429
33.3%
1 25774
 
5.4%

Moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
156075 
1.0
 
2354

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 156075
98.5%
1.0 2354
 
1.5%

Length

2024-07-05T13:49:19.988390image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:20.140411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 156075
98.5%
1.0 2354
 
1.5%

Most occurring characters

ValueCountFrequency (%)
0 314504
66.2%
. 158429
33.3%
1 2354
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 314504
99.3%
1 2354
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 314504
66.2%
. 158429
33.3%
1 2354
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 314504
66.2%
. 158429
33.3%
1 2354
 
0.5%

Moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
147411 
1.0
 
11018

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 147411
93.0%
1.0 11018
 
7.0%

Length

2024-07-05T13:49:20.306280image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:20.455137image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 147411
93.0%
1.0 11018
 
7.0%

Most occurring characters

ValueCountFrequency (%)
0 305840
64.3%
. 158429
33.3%
1 11018
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 305840
96.5%
1 11018
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 305840
64.3%
. 158429
33.3%
1 11018
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 305840
64.3%
. 158429
33.3%
1 11018
 
2.3%

Moti_pickupdropoff_goods
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
113987 
1.0
44442 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 113987
71.9%
1.0 44442
 
28.1%

Length

2024-07-05T13:49:20.620702image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:20.770348image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 113987
71.9%
1.0 44442
 
28.1%

Most occurring characters

ValueCountFrequency (%)
0 272416
57.3%
. 158429
33.3%
1 44442
 
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 272416
86.0%
1 44442
 
14.0%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 272416
57.3%
. 158429
33.3%
1 44442
 
9.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 272416
57.3%
. 158429
33.3%
1 44442
 
9.4%

Moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
83588 
1.0
74841 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 83588
52.8%
1.0 74841
47.2%

Length

2024-07-05T13:49:20.937075image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:21.172310image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 83588
52.8%
1.0 74841
47.2%

Most occurring characters

ValueCountFrequency (%)
0 242017
50.9%
. 158429
33.3%
1 74841
 
15.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 242017
76.4%
1 74841
 
23.6%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 242017
50.9%
. 158429
33.3%
1 74841
 
15.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 242017
50.9%
. 158429
33.3%
1 74841
 
15.7%

Main_moti_work
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
126184 
1.0
32245 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 126184
79.6%
1.0 32245
 
20.4%

Length

2024-07-05T13:49:21.341122image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:21.541462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 126184
79.6%
1.0 32245
 
20.4%

Most occurring characters

ValueCountFrequency (%)
0 284613
59.9%
. 158429
33.3%
1 32245
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 284613
89.8%
1 32245
 
10.2%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 284613
59.9%
. 158429
33.3%
1 32245
 
6.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 284613
59.9%
. 158429
33.3%
1 32245
 
6.8%

Main_moti_profession
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
153482 
1.0
 
4947

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 153482
96.9%
1.0 4947
 
3.1%

Length

2024-07-05T13:49:21.721507image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:21.888262image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 153482
96.9%
1.0 4947
 
3.1%

Most occurring characters

ValueCountFrequency (%)
0 311911
65.6%
. 158429
33.3%
1 4947
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 311911
98.4%
1 4947
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 311911
65.6%
. 158429
33.3%
1 4947
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 311911
65.6%
. 158429
33.3%
1 4947
 
1.0%

Main_moti_pickupdropoff_person
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
149201 
1.0
 
9228

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 149201
94.2%
1.0 9228
 
5.8%

Length

2024-07-05T13:49:22.041520image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:22.205109image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 149201
94.2%
1.0 9228
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 307630
64.7%
. 158429
33.3%
1 9228
 
1.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 307630
97.1%
1 9228
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 307630
64.7%
. 158429
33.3%
1 9228
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 307630
64.7%
. 158429
33.3%
1 9228
 
1.9%

Main_moti_pickupdropoff_goods
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
109595 
1.0
48834 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 109595
69.2%
1.0 48834
30.8%

Length

2024-07-05T13:49:22.388607image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:22.622908image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 109595
69.2%
1.0 48834
30.8%

Most occurring characters

ValueCountFrequency (%)
0 268024
56.4%
. 158429
33.3%
1 48834
 
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268024
84.6%
1 48834
 
15.4%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 268024
56.4%
. 158429
33.3%
1 48834
 
10.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 268024
56.4%
. 158429
33.3%
1 48834
 
10.3%

Main_moti_sparetime
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.2 MiB
0.0
95254 
1.0
63175 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters475287
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 95254
60.1%
1.0 63175
39.9%

Length

2024-07-05T13:49:22.817237image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-05T13:49:23.043465image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 95254
60.1%
1.0 63175
39.9%

Most occurring characters

ValueCountFrequency (%)
0 253683
53.4%
. 158429
33.3%
1 63175
 
13.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 316858
66.7%
Other Punctuation 158429
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 253683
80.1%
1 63175
 
19.9%
Other Punctuation
ValueCountFrequency (%)
. 158429
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 475287
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 253683
53.4%
. 158429
33.3%
1 63175
 
13.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 475287
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 253683
53.4%
. 158429
33.3%
1 63175
 
13.3%

Interactions

2024-07-05T13:49:04.861494image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:58.321858image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:59.758783image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:00.875285image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:02.341868image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:03.524854image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:05.107634image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:58.509266image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:59.933602image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:01.058451image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:02.599195image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:03.724917image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:05.294185image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:58.683796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:00.108605image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:01.241796image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:02.759105image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:03.908127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:05.527457image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:58.942421image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:00.305200image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:01.540127image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:02.958651image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:04.115211image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:05.711590image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:59.361386image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:00.488411image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:01.790375image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:03.141608image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:04.307888image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:05.890955image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:48:59.558935image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:00.675462image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:02.025331image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:03.347939image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
2024-07-05T13:49:04.524902image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/

Correlations

2024-07-05T13:49:23.243121image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
AgeDisposable_income_householdEL_higherprofessional_universityEL_primaryEL_secondary_higherEL_secondary_lowerEbike_in_HHEthn_dutchEthn_nonwesternEthn_westernGender_maleHHC_coupleHHC_couple_with_childrenHHC_onepersonHHC_oneperson_with_childrenMain_moti_pickupdropoff_goodsMain_moti_pickupdropoff_personMain_moti_professionMain_moti_sparetimeMain_moti_workMoti_pickupdropoff_goodsMoti_pickupdropoff_personMoti_professionMoti_sparetimeMoti_workNumber_of_cars_in_HHPW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hPart_of_sequenceStarting_postalcodeTrip_distanceTrip_starthourTrip_transportation_typeUO_benefitsUO_noneUO_student/scholar
Age1.000-0.1550.3820.7620.2660.3030.2540.1530.1730.0440.0900.5110.5170.2130.1810.0500.0650.0190.0860.0380.1720.2150.0500.2790.201-0.0150.6900.2340.2260.5700.0730.0420.101-0.0690.2130.6750.6550.876
Disposable_income_household-0.1551.0000.1830.0580.0900.2190.1200.0980.0760.0630.0460.1890.4610.4950.1600.0310.0230.0110.0210.0100.0900.0820.0110.0280.0410.4230.2240.0530.0830.1890.036-0.0350.0470.0150.0770.2900.2020.174
EL_higherprofessional_university0.3820.1831.0000.3550.5730.3380.0900.0060.0440.0380.0000.0490.0570.0670.0790.0190.0180.0150.0290.0080.0180.0720.0090.0730.030-0.0200.2900.0710.0220.3020.003-0.0640.0460.0430.0720.0890.3180.292
EL_primary0.7620.0580.3551.0000.2590.1530.0330.0880.1030.0110.0080.1640.1930.1070.0850.0290.0120.0150.0540.0190.0890.0660.0230.1980.105-0.0000.3480.0080.1280.2590.002-0.016-0.135-0.0150.2370.1070.3050.502
EL_secondary_higher0.2660.0900.5730.2591.0000.2470.0390.0160.0000.0220.0060.0290.0130.0000.0280.0020.0030.0160.0090.0100.0140.0020.0250.0610.0590.0500.0690.0560.0950.0220.0000.0420.050-0.0020.1040.0030.0060.002
EL_secondary_lower0.3030.2190.3380.1530.2471.0000.1150.0600.0410.0380.0000.1380.1320.0110.0080.0040.0090.0170.0000.0070.0460.0350.0010.0120.016-0.0380.1660.0190.0310.1510.0030.0540.002-0.0470.0090.2490.1660.080
Ebike_in_HH0.2540.1200.0900.0330.0390.1151.0000.1670.1480.0700.0150.1440.0120.1520.0550.0060.0000.0060.0040.0000.0030.0110.0040.0180.0220.1400.0810.0060.0210.0990.0250.1000.047-0.0270.0940.1460.0800.068
Ethn_dutch0.1530.0980.0060.0880.0160.0600.1671.0000.6900.6350.0150.0790.0120.0360.0650.0090.0090.0120.0060.0020.0060.0080.0000.0150.0060.1560.0140.0030.0370.0110.0580.1650.035-0.0020.1330.0470.0160.074
Ethn_nonwestern0.1730.0760.0440.1030.0000.0410.1480.6901.0000.1220.0020.1110.0560.0060.0840.0000.0100.0040.0060.0000.0010.0090.0040.0110.010-0.1170.0170.0150.0330.0000.059-0.151-0.028-0.0020.1440.0620.0220.100
Ethn_western0.0440.0630.0380.0110.0220.0380.0700.6350.1221.0000.0180.0100.0420.0430.0000.0100.0010.0110.0000.0060.0110.0000.0060.0080.000-0.0900.0000.0100.0160.0140.017-0.065-0.0180.0050.0350.0000.0000.003
Gender_male0.0900.0460.0000.0080.0060.0000.0150.0150.0020.0181.0000.0570.0040.0140.0750.0110.0110.0110.0000.0140.0200.0340.0280.0000.0350.0250.0000.0690.2550.2120.0040.0030.1080.0120.0990.0150.0390.031
HHC_couple0.5110.1890.0490.1640.0290.1380.1440.0790.1110.0100.0571.0000.5930.3140.1740.0180.0300.0050.0080.0040.0790.0990.0080.0100.010-0.0380.1030.0480.0700.0320.0000.0310.064-0.0210.0700.2940.0530.273
HHC_couple_with_children0.5170.4610.0570.1930.0130.1320.0120.0120.0560.0420.0040.5931.0000.4280.2370.0310.0470.0000.0130.0070.1170.1560.0100.0120.0150.3930.1350.0510.1050.0390.0230.008-0.031-0.0150.0670.2920.0880.227
HHC_oneperson0.2130.4950.0670.1070.0000.0110.1520.0360.0060.0430.0140.3140.4281.0000.1260.0260.0290.0020.0120.0030.0640.0870.0000.0140.000-0.3990.0390.0270.0660.0200.021-0.031-0.0220.0410.0740.0750.0220.059
HHC_oneperson_with_children0.1810.1600.0790.0850.0280.0080.0550.0650.0840.0000.0750.1740.2370.1261.0000.0110.0060.0030.0080.0000.0120.0080.0090.0180.011-0.0930.0210.0300.0240.0500.008-0.026-0.0220.0040.0510.0760.0450.144
Main_moti_pickupdropoff_goods0.0500.0310.0190.0290.0020.0040.0060.0090.0000.0100.0110.0180.0310.0260.0111.0000.1660.1200.5440.3370.1330.0190.0240.0600.060-0.0210.0090.0100.0070.0100.000-0.002-0.025-0.0300.0320.0400.0050.038
Main_moti_pickupdropoff_person0.0650.0230.0180.0120.0030.0090.0000.0090.0100.0010.0110.0300.0470.0290.0060.1661.0000.0450.2020.1260.0170.1630.0060.0460.0260.0150.0230.0080.0310.0030.016-0.012-0.005-0.0250.0320.0050.0190.031
Main_moti_profession0.0190.0110.0150.0150.0160.0170.0060.0120.0040.0110.0110.0050.0000.0020.0030.1200.0451.0000.1460.0910.0170.0080.1690.0190.0010.0140.0310.0040.0170.0170.0010.0310.013-0.0010.0260.0030.0180.018
Main_moti_sparetime0.0860.0210.0290.0540.0090.0000.0040.0060.0060.0000.0000.0080.0130.0120.0080.5440.2020.1461.0000.4120.0720.0340.0260.1330.0600.0030.0610.0170.0350.0420.001-0.009-0.0090.0140.0470.0130.0540.082
Main_moti_work0.0380.0100.0080.0190.0100.0070.0000.0020.0000.0060.0140.0040.0070.0030.0000.3370.1260.0910.4121.0000.0470.0280.0100.0570.1580.0050.0580.0060.0090.0540.0080.0070.0360.0320.0280.0310.0530.030
Moti_pickupdropoff_goods0.1720.0900.0180.0890.0140.0460.0030.0060.0010.0110.0200.0790.1170.0640.0120.1330.0170.0170.0720.0471.0000.1710.0770.5910.275-0.0680.0430.0130.0000.0390.082-0.004-0.237-0.0090.0830.1380.0300.122
Moti_pickupdropoff_person0.2150.0820.0720.0660.0020.0350.0110.0080.0090.0000.0340.0990.1560.0870.0080.0190.1630.0080.0340.0280.1711.0000.0330.2590.1200.0530.0820.0190.0630.0430.037-0.006-0.024-0.0510.1320.0060.0780.104
Moti_profession0.0500.0110.0090.0230.0250.0010.0040.0000.0040.0060.0280.0080.0100.0000.0090.0240.0060.1690.0260.0100.0770.0331.0000.1160.0540.0270.0660.0110.0310.0390.0240.0090.0550.0120.0750.0260.0470.028
Moti_sparetime0.2790.0280.0730.1980.0610.0120.0180.0150.0110.0080.0000.0100.0120.0140.0180.0600.0460.0190.1330.0570.5910.2590.1161.0000.417-0.0080.2080.0180.0890.1510.0520.0060.0440.1300.2720.0060.1880.241
Moti_work0.2010.0410.0300.1050.0590.0160.0220.0060.0100.0000.0350.0100.0150.0000.0110.0600.0260.0010.0600.1580.2750.1200.0540.4171.0000.0490.2560.0000.0640.2100.063-0.0020.227-0.1340.2120.1560.2220.096
Number_of_cars_in_HH-0.0150.423-0.020-0.0000.050-0.0380.1400.156-0.117-0.0900.025-0.0380.393-0.399-0.093-0.0210.0150.0140.0030.005-0.0680.0530.027-0.0080.0491.0000.1610.0410.0680.1050.0360.1270.136-0.0140.1200.1540.1290.066
PW_no0.6900.2240.2900.3480.0690.1660.0810.0140.0170.0000.0000.1030.1350.0390.0210.0090.0230.0310.0610.0580.0430.0820.0660.2080.2560.1611.0000.1680.3470.6820.0180.022-0.125-0.0440.2010.5420.7990.367
PW_yeslessthan12h0.2340.0530.0710.0080.0560.0190.0060.0030.0150.0100.0690.0480.0510.0270.0300.0100.0080.0040.0170.0060.0130.0190.0110.0180.0000.0410.1681.0000.0850.1670.0130.010-0.0190.0110.0750.0290.1260.191
PW_yesmorethan12to30h0.2260.0830.0220.1280.0950.0310.0210.0370.0330.0160.2550.0700.1050.0660.0240.0070.0310.0170.0350.0090.0000.0630.0310.0890.0640.0680.3470.0851.0000.3460.0140.035-0.009-0.0090.0410.1210.1810.085
PW_yesmorethan30h0.5700.1890.3020.2590.0220.1510.0990.0110.0000.0140.2120.0320.0390.0200.0500.0100.0030.0170.0420.0540.0390.0430.0390.1510.2100.1050.6820.1670.3461.0000.013-0.0510.1400.0460.2000.4430.7180.382
Part_of_sequence0.0730.0360.0030.0020.0000.0030.0250.0580.0590.0170.0040.0000.0230.0210.0080.0000.0160.0010.0010.0080.0820.0370.0240.0520.0630.0360.0180.0130.0140.0131.0000.034-0.0530.2850.1710.0120.0250.045
Starting_postalcode0.042-0.035-0.064-0.0160.0420.0540.1000.165-0.151-0.0650.0030.0310.008-0.031-0.026-0.002-0.0120.031-0.0090.007-0.004-0.0060.0090.006-0.0020.1270.0220.0100.035-0.0510.0341.0000.043-0.0110.0870.0550.0320.038
Trip_distance0.1010.0470.046-0.1350.0500.0020.0470.035-0.028-0.0180.1080.064-0.031-0.022-0.022-0.025-0.0050.013-0.0090.036-0.237-0.0240.0550.0440.2270.136-0.125-0.019-0.0090.140-0.0530.0431.000-0.0500.1580.0420.0790.052
Trip_starthour-0.0690.0150.043-0.015-0.002-0.047-0.027-0.002-0.0020.0050.012-0.021-0.0150.0410.004-0.030-0.025-0.0010.0140.032-0.009-0.0510.0120.130-0.134-0.014-0.0440.011-0.0090.0460.285-0.011-0.0501.0000.0610.1660.1410.079
Trip_transportation_type0.2130.0770.0720.2370.1040.0090.0940.1330.1440.0350.0990.0700.0670.0740.0510.0320.0320.0260.0470.0280.0830.1320.0750.2720.2120.1200.2010.0750.0410.2000.1710.0870.1580.0611.0000.0860.2060.312
UO_benefits0.6750.2900.0890.1070.0030.2490.1460.0470.0620.0000.0150.2940.2920.0750.0760.0400.0050.0030.0130.0310.1380.0060.0260.0060.1560.1540.5420.0290.1210.4430.0120.0550.0420.1660.0861.0000.6530.341
UO_none0.6550.2020.3180.3050.0060.1660.0800.0160.0220.0000.0390.0530.0880.0220.0450.0050.0190.0180.0540.0530.0300.0780.0470.1880.2220.1290.7990.1260.1810.7180.0250.0320.0790.1410.2060.6531.0000.489
UO_student/scholar0.8760.1740.2920.5020.0020.0800.0680.0740.1000.0030.0310.2730.2270.0590.1440.0380.0310.0180.0820.0300.1220.1040.0280.2410.0960.0660.3670.1910.0850.3820.0450.0380.0520.0790.3120.3410.4891.000

Missing values

2024-07-05T13:49:06.240252image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-05T13:49:07.341156image/svg+xmlMatplotlib v3.8.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
08423.0172111030.03.07.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
18423.0172111030.03.08.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
28423.0172111030.03.09.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
38423.0172111030.03.012.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
48423.0172111030.03.013.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
58423.0172111030.03.017.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.00.01.00.01.00.00.00.00.00.00.00.00.01.0
68044.0047201048.03.07.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
78044.0047201010.03.09.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
88043.0047201010.03.09.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
98044.0047201048.03.012.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.0
Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime
1584191607.0125211050.01.06.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.01.00.0
1584201606.0125211040.01.08.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.00.01.00.00.00.01.00.0
1584211607.01252110100.01.09.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
158422NaN1252110100.01.011.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
1584231607.01252110120.01.012.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
1584241611.01252110120.01.013.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
1584251607.0125211040.01.014.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.0
1584261606.0125211040.01.018.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.01.00.00.00.00.00.00.01.00.0
1584271607.0125211060.01.019.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0
1584281613.0125211060.01.020.010.00.00.01.01.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.01.00.0

Duplicate rows

Most frequently occurring

Starting_postalcodeGender_maleAgeNumber_of_cars_in_HHEbike_in_HHDisposable_income_householdTrip_distanceTrip_transportation_typeTrip_starthourPart_of_sequencePW_noPW_yeslessthan12hPW_yesmorethan12to30hPW_yesmorethan30hUO_noneUO_benefitsUO_student/scholarHHC_onepersonHHC_coupleHHC_couple_with_childrenHHC_oneperson_with_childrenEthn_dutchEthn_westernEthn_nonwesternEL_primaryEL_secondary_lowerEL_secondary_higherEL_higherprofessional_universityMoti_workMoti_professionMoti_pickupdropoff_personMoti_pickupdropoff_goodsMoti_sparetimeMain_moti_workMain_moti_professionMain_moti_pickupdropoff_personMain_moti_pickupdropoff_goodsMain_moti_sparetime# duplicates
4401411.01442024.04.013.001.00.00.00.00.01.00.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.01.00.011
17873023.016410130.01.013.001.00.00.00.00.01.00.01.00.00.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.01.011
34815044.0110100101.04.012.001.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.01.09
34825044.0110100101.04.016.011.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.00.00.00.00.01.07
34024881.0170141.03.08.001.00.00.00.00.00.01.00.00.01.00.00.00.01.01.00.00.00.00.00.00.00.01.01.00.00.00.00.06
3401241.0091091.04.011.001.00.00.00.00.00.01.00.00.01.00.01.00.00.01.00.00.00.00.00.00.00.01.01.00.00.00.00.05
4071351.015921914.01.09.010.00.00.01.00.01.00.00.01.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.00.00.01.00.05
12672518.00751131.04.013.011.00.00.00.00.01.00.00.01.00.00.01.00.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.05
53919872.006431103.04.011.010.00.01.00.01.00.00.00.01.00.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.00.00.01.05
19093067.00680014.04.013.011.00.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.01.00.00.00.00.01.00.04